Kroenke Christopher D, Bretthorst G Larry, Inder Terrie E, Neil Jeffrey J
Department of Radiology, Washington University, St. Louis, Missouri 63110, USA.
Magn Reson Med. 2006 Jan;55(1):187-97. doi: 10.1002/mrm.20728.
An active area of research involves optimally modeling brain diffusion MRI data for various applications. In this study Bayesian analysis procedures were used to evaluate three models applied to phase-sensitive diffusion MRI data obtained from formalin-fixed perinatal primate brain tissue: conventional diffusion tensor imaging (DTI), a cumulant expansion, and a family of modified DTI expressions. In the latter two cases the optimum expression was selected from the model family for each voxel in the image. The ability of each model to represent the data was evaluated by comparing the magnitude of the residuals to the thermal noise. Consistent with previous findings from other laboratories, the DTI model poorly represented the experimental data. In contrast, the cumulant expansion and modified DTI expressions were both capable of modeling the data to within the noise using six to eight adjustable parameters per voxel. In these cases the model selection results provided a valuable form of image contrast. The successful modeling procedures differ from the conventional DTI model in that they allow the MRI signal to decay to a positive offset. Intuitively, the positive offset can be thought of as spins that are sufficiently restricted to appear immobile over the sampled range of b-values.
一个活跃的研究领域涉及针对各种应用对脑扩散磁共振成像（MRI）数据进行优化建模。在本研究中，采用贝叶斯分析程序来评估应用于从福尔马林固定的围产期灵长类动物脑组织获取的相敏扩散MRI数据的三种模型：传统扩散张量成像（DTI）、累积量展开以及一族修正的DTI表达式。在后两种情况下，针对图像中的每个体素从模型族中选择最优表达式。通过将残差的大小与热噪声进行比较来评估每个模型表示数据的能力。与其他实验室先前的研究结果一致，DTI模型对实验数据的表示较差。相比之下，累积量展开和修正的DTI表达式都能够使用每个体素六到八个可调参数将数据建模到噪声范围内。在这些情况下，模型选择结果提供了一种有价值的图像对比度形式。成功的建模程序与传统DTI模型的不同之处在于，它们允许MRI信号衰减到一个正偏移量。直观地说，正偏移量可以被认为是在采样的b值范围内受到充分限制而显得静止不动的自旋。
